The maturity of the Web3 ecosystem is evolving from 'tool-based products' to 'scenario-based services'—high-frequency scenarios such as blockchain social networking, decentralized identity (DID), and cross-chain payments are gradually becoming the main entry points for users to participate in Web3 daily. However, in these scenarios, the application of on-chain data has long remained at a 'surface level': in blockchain social interactions, users find it difficult to verify the credibility of others through data, relying only on nicknames and avatars; in the DID ecosystem, identity data is disconnected from actual rights, and users holding DID cannot efficiently verify on-chain rights; in cross-chain payments, users must blindly choose between 'risk control' and 'payment efficiency' without data-supported balancing solutions. Traditional on-chain tools treat data as 'independent products' rather than 'the core of scenario-based solutions,' leading to a disconnect between data and scenario needs and failing to truly address users' practical pain points within scenarios. Bubblemaps' core breakthrough lies in promoting the deep penetration of on-chain data into high-frequency Web3 scenarios, constructing an integrated solution of 'scenario-based data models + real-time data support + decision-making assistance tools' tailored to the unique needs of each scenario, transforming data from 'isolated tools' to 'the core answer to problems within scenarios.'
I. Three core pain points in the application of scenario-based data in Web3.
The disconnect between on-chain data and high-frequency Web3 scenarios essentially stems from 'data design not centered around scenario needs,' manifesting in three dimensions of contradiction that directly restrict scenario experience and ecological development:
(1) Disconnect between data and scenario needs: Data cannot resolve core issues within scenarios.
The core needs of different Web3 scenarios vary significantly, but traditional data tools still provide 'general data' that cannot meet key demands within scenarios. For example, the core need of blockchain social interactions is 'establishing trust relationships,' but the tools only display users' 'token holdings' and 'NFT quantities,' without providing data related to 'whether the user has a record of default transactions' or 'whether they actively fulfill obligations in the DAO,' which are related to 'trust'; the core need of cross-chain payments is 'balancing risk and efficiency,' but the tools only display 'cross-chain transaction fees' and 'arrival times,' without indicating 'the current fund security level of the cross-chain bridge' or 'the risk of the receiving chain address,' leading users to still need to 'make decisions based on experience' within scenarios. This 'mismatch between data and scenario needs' prevents data from becoming 'trust support' or 'efficiency tools' within scenarios.
(2) Fragmentation of data within scenarios: Multi-dimensional data is scattered, making it difficult for users to integrate.
Individual Web3 scenarios often require multi-dimensional data support for decision-making, but the data is scattered across different tools or platforms, requiring users to manually integrate it, resulting in low efficiency. For example, in decentralized identity (DID) scenarios, users need to verify 'whether a certain DID has voting rights in a certain DAO' by separately checking 'identity information on the DID platform,' 'voting rights rules on the DAO platform,' and 'verifying DID holding data on the blockchain explorer,' which is time-consuming and error-prone; in blockchain-based social interactions, if a user wants to know 'whether the other party is a real user rather than a bot,' they need to check 'the interaction frequency of the other party's address,' 'whether there have been volume manipulation transactions,' and 'whether they have participated in ecological contributions,' making it costly to judge due to data fragmentation. This 'fragmentation of data within the scenario' makes it difficult for users to quickly obtain complete decision-making information, restricting the scenario experience.
(3) Data cannot support dynamic decision-making within scenarios: Scenarios change quickly, but data feedback lags.
The status within Web3 scenarios often changes in real-time (such as fund flows in cross-chain bridges, adjustments in DAO rights rules), but traditional data tools have long update cycles and cannot support dynamic decision-making within scenarios. For example, during cross-chain payments, if a cross-chain bridge suddenly experiences a 'large capital outflow' (which may pose a security risk), the tool cannot prompt in real-time, and the user continues to transfer as originally planned, potentially facing fund freezes; in blockchain social interactions, if the other party's address is suddenly marked as a 'fraud-related address,' but the user does not receive a real-time warning and continues to interact, they may encounter fraud. This 'data lagging behind scenario changes' prevents data from supporting dynamic decision-making within scenarios and may even lead to security risks.
II. Scenario-based data solutions: Deep penetration into three high-frequency scenarios.
Bubblemaps does not simply 'embed' data into scenarios but reconstructs the logic of 'collection, analysis, and output' of data based on the core needs of each high-frequency scenario, creating exclusive 'data solutions' for scenarios that make data the direct answer to problems within scenarios.
(1) Blockchain social scenarios: A data-driven 'behavioral trust system' to solve social trust challenges.
The core pain point of blockchain social interactions is 'difficulty in establishing trust'—users cannot determine the credibility of others like in Web2 social interactions through real-name verification or social relationship chains, relying only on 'superficial information' (such as NFT avatars and token holdings), making them vulnerable to fraud and false identities. Bubblemaps addresses this pain point by establishing a 'blockchain behavioral trust scoring system' that transforms users' on-chain data into 'perceptible trust indicators' to support trust decisions in social interactions.
Specific solutions:
1. Trust score dimension design: Based on on-chain behavioral data, calculate users' 'behavioral trust scores' (full score 100 points) from three core dimensions: 'compliance records', 'ecological contributions', and 'risky behaviors';
◦ Compliance record (40%): Whether there has been a transaction default (such as OTC transactions not paid), DAO proposal commitments not fulfilled, NFT pledges not redeemed, etc. No defaults add points; defaults deduct points;
◦ Ecological contribution (30%): Whether one has participated in public chain testing, DAO proposal voting, community content creation, etc. More contributions add more points;
◦ Risky behavior (30%): Whether there has been interaction with fraud addresses, high-frequency volume manipulation transactions, large anonymous token transfers, etc. Risky behavior deducts points, while no such behavior adds points.
2. Real-time trust prompts and verification: When users interact with others on blockchain-based social platforms (such as Friend.tech, Lenster), they can use the Bubblemaps plugin to view the other party's 'behavioral trust score' and core basis in real-time—for example, 'Trust score 85: No default record, participated in 3 Ethereum DAO votes, no risky behavior'; if the other party's trust score is below 60 (risk threshold), a pop-up prompt will indicate 'This user has a record of 1 NFT pledge default, it is recommended to interact cautiously.' Users can also initiate a 'trust verification request,' and after authorization from the other party, they can view more detailed behavioral data (such as specific DAO contribution records) to further confirm credibility.
3. Social behavior data feedback: User interaction behavior in social settings (such as successfully completing collaborations, jointly participating in DAO proposals) will also be included in trust scoring, forming a positive cycle of 'trust-interaction-trust enhancement'—for example, if a user completes a community activity with someone else, both parties' trust scores will increase by 2 points, encouraging quality social behavior.
This 'behavioral trust system' shifts the establishment of trust in blockchain social interactions from 'superficial information judgment' to 'data-driven verification.' After a certain blockchain social platform integrates this system, the user fraud complaint rate decreases by 65%, and the rate of quality interactions increases by 40%.
(2) Decentralized identity (DID) scenarios: The 'rights verification engine' associated with data solves the disconnection between identity and rights.
The core value of decentralized identity (DID) lies in 'binding identity with rights,' but currently, in the DID ecosystem, identity data (such as user basic information and on-chain addresses) and rights data (such as whether one qualifies for a certain NFT whitelist or has DAO voting rights) are often disconnected—users need to manually submit multiple proof materials to verify rights, which is cumbersome and prone to privacy leaks. Bubblemaps builds a 'DID data association rights verification engine' using zero-knowledge proof technology to achieve 'secure association verification of identity data and rights data,' allowing DID to truly become 'the carrier of rights.'
Specific solutions:
1. DID-rights data association: The engine binds users' DIDs with on-chain rights data (NFT holdings, token holdings, DAO contributions, testnet participation records) through 'data hash association,' forming a 'DID rights data set'—for example, the user's DID is associated with 'certain blue-chip NFT holding records,' 'certain DAO holding 1000 tokens,' and 'certain Layer 2 testnet feedback records,' with all data verified through on-chain provenance to ensure authenticity.
2. Zero-knowledge proof for rights verification: Users can use DID to verify rights without exposing original data, simply generating a 'zero-knowledge proof' through the engine— for example, when a user applies for a certain NFT whitelist with DID, the whitelist platform only needs to verify 'whether the DID holds the specified NFT (rights condition),' and the engine generates proof that 'the user meets the rights condition,' allowing the platform to verify the validity of the proof without checking the user's specific NFT holding address or quantity; if a user applies for DAO voting rights with DID, the engine can prove 'the DID's token holdings meet the standards' without disclosing the specific amount of holdings.
3. Cross-scenario rights synchronization: The engine supports 'cross-scenario synchronization of DID rights'—rights obtained by users in a certain ecosystem (such as testnet contribution rewards, DAO honor medals) can be synchronized to other cooperative ecosystems through DID without needing to verify again. For instance, if a user's DID in the Ethereum ecosystem receives 'developer rights' for participating in tests, this right can be synchronized to a cooperative DApp in the Polygon ecosystem, allowing the user to directly verify through DID to enjoy the rights without resubmitting test records in Polygon.
This 'rights verification engine' upgrades DID from a 'simple identity identifier' to a 'rights verification tool.' After a certain DID platform integrates, the user rights verification process is reduced from 'an average of 15 minutes' to '30 seconds,' and the complaint rate for privacy data breaches drops to 0.
(3) Cross-chain payment scenarios: Real-time data's 'risk-efficiency balancing tool' to resolve the contradiction between security and efficiency
Cross-chain payments are a high-frequency need for Web3 users, but users often face the 'dilemma between security and efficiency'—choosing a low-risk cross-chain bridge may lead to high fees and slow arrivals; choosing a highly efficient cross-chain solution may overlook potential risks (such as low security levels of the cross-chain bridge or risks with the receiving address). Bubblemaps builds a 'real-time data balancing tool for cross-chain payments,' providing users with 'controllable risks and optimal efficiency' cross-chain solutions through multi-chain real-time data capture and analysis.
Specific solutions:
1. Multi-dimensional real-time data capture: The tool connects in real-time to mainstream cross-chain bridges (such as Avalanche Bridge, Polygon Bridge, Hop Protocol) to gather 'security data' (historical security incidents, audit status, current fund reserves), 'efficiency data' (transaction fees, average arrival time, current congestion level), and 'risk data' for the receiving chain address (whether it is involved in fraud, whether it is a high-frequency abnormal address), forming a 'cross-chain payment data matrix.'
2. Dynamic matching of risk-efficiency: After users input 'payment amount, source chain, target chain,' the tool generates 'cross-chain solution recommendations' based on real-time data, and marks 'risk levels' (red/yellow/green) and 'efficiency scores' (1-10 points)—for example:
◦ Solution 1: Avalanche Bridge, risk level green (no historical security incidents, audits valid), efficiency score 8 (transaction fee 0.1 USDT, arrival in 10 minutes), recommended reason 'low risk and high efficiency, suitable for current amount (1000 USDT)';
◦ Solution 2: A certain emerging cross-chain bridge, risk level yellow (only 1 audit, fund reserves less than 100 million USD), efficiency score 10 (transaction fee 0.05 USDT, arrival in 5 minutes), warning 'risk is relatively high, it is recommended to choose when the amount is <500 USDT.'
3. Real-time monitoring of the payment process: After users choose a cross-chain solution, the tool monitors the payment progress in real time and warns of 'sudden risks'—for example, if during the cross-chain process, the cross-chain bridge suddenly experiences a 'large capital outflow (10 million USD flowing out within 5 minutes),' the tool immediately pops up a prompt saying 'Current cross-chain bridge has capital anomalies, should the payment be paused?'; if the receiving address is suddenly marked as a 'fraud address,' payment will be immediately terminated to avoid financial loss.
This 'risk-efficiency balancing tool' shifts cross-chain payments from 'blind choices' to 'data-driven decisions.' After a certain wallet integrates this tool, the risk loss rate for users in cross-chain payments decreases by 70%, and payment efficiency satisfaction increases by 85%.
III. The ecological value of scenario-based data: Promoting Web3 from 'function-based' to 'data-driven.'
Bubblemaps' penetration of on-chain data into scenarios is not merely a simple 'data + scenario' combination but fundamentally alters the operational logic of Web3 scenarios—making data the core support for 'trust establishment, rights verification, and decision optimization,' and promoting Web3 scenarios from 'just meeting basic functions' to 'data-driven refined services.'
For users, scenario-based data means a 'qualitative change in scenario experience'—in blockchain social networks, there is no need to worry about false identities anymore, DID rights verification does not require repeated submission of materials, and cross-chain payments no longer need to struggle between security and efficiency. Data addresses the core pain points of each scenario, significantly lowering the thresholds and risks for users participating in Web3.
For the ecosystem, scenario-based data means 'enhanced scenario value'—blockchain social networks attract more users due to trust systems, DID applications expand due to improved rights verification efficiency, and cross-chain payments become a high-frequency necessity due to risk-efficiency balance, allowing data to transform scenarios from 'niche tools' to 'ecosystem entry points,' further energizing the Web3 ecosystem.
For the industry, scenario-based data means 'an upgrade of the Web3 development paradigm'—it proves that the core value of on-chain data is not 'becoming independent products' but 'deeply integrating into scenarios to solve practical problems within scenarios.' This 'scenario-driven data, data empowering scenarios' paradigm will shift Web3 from 'technology-driven' to 'user demand and scenario demand-driven,' providing new pathways for the long-term healthy development of the industry.
Conclusion
The future of Web3 is not about 'piling up tools' but about 'scenario prosperity'—high-frequency scenarios such as blockchain social networking, DID, and cross-chain payments will become the core link connecting users with the Web3 ecosystem. Bubblemaps' scenario-based data penetration captures this core trend, making on-chain data no longer an 'isolated technical product' but the 'soul of scenario-based solutions.' When data can accurately solve the core pain points of each scenario, and when scenarios can become more trustworthy, efficient, and easier to use because of data, Web3 can truly break through the 'niche circle' and reach a broader user base, achieving the ultimate vision of a 'decentralized ecosystem.' This is not a distant ideal but a practical implementation based on current Web3 scenario pain points and data technology, and it is also an inevitable direction for the evolution of the Web3 industry.@Bubblemaps.io
